James Wen
ABSTRACT
Providing the most appropriate navigation information on mobile devices for pedestrians requires an understanding of how pedestrians use navigation technology. While large-scale studies have identified different types of pedestrian navigation behaviour, far less data exists for classifying navigators by the technology they use. We report on a study that presented pedestrian users with multiple navigation interfaces in order to gain insight on usage preferences. We create a classification of users based on observed usage behavior that would be helpful for designing pedestrian navigation aids.
- Arning, K. et al. Insights into user experiences and acceptance of mobile indoor navigation devices. Mobile and Ubiquitous Multimedia. (2012), 1--10. Google Scholar
Digital Library
- D'Orazio, M. J. and Lueg, C. Peg Hunting: Foraging with Macro- and Micro-Navigation. Oz CHI (2012), 89--92. Google ScholarDigital Library
- Dünser, A. et al. Exploring the use of Handheld AR for Outdoor Navigation. Computers & Graphics. 36, (2012), 1084--1095. Google ScholarDigital Library
- Li, C. User preferences, information transactions and location-based services: A study of urban pedestrian wayfinding. Computers, Environment and Urban Systems. 30, 6 (Nov. 2006), 726--740.Google ScholarCross Ref
- Millonig, A. and Gartner, G. Identifying motion and interest patterns of shoppers for developing personalised wayfinding tools. Journal of Location Based Services. 5, 1 (Mar. 2011), 3--21. Google ScholarDigital Library
- Redden, E. S. et al. Cognitive Load Study Using Increasingly Immersive Levels of Map-based Information Portrayal on the End User Device. Army Research Laboratory, Fort Benning (2012).Google Scholar
- Rehrl, K. et al. Pedestrian Navigation with Augmented Reality, Voice and Digital Map: Results from a Field Study assessing Performance and User Experience. International Symposium on Location-based Services (2011), 3--20.Google Scholar
- Seager, W. and Fraser, D. S. Comparing physical, automatic and manual map rotation for pedestrian navigation. CHI '07. (2007), 767--776. Google ScholarDigital Library
- Webber, E. et al. Pedestrian Navigation with a Mobile Device: Strategy Use and Environmental Learning. BCS HCI (2012), 286--291. Google ScholarDigital Library
- Wen, J. et al. A Study of User Perception, Interface Performance, and Actual Usage of Mobile Pedestrian Navigation Aides. Proceedings of the Human Factors and Ergonomics Society Annual Meeting September (2013), 1958--1962.Google Scholar
Index Terms
- Classifying users of mobile pedestrian navigation tools
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